Peripheral Ulcerative Keratitis Associated with Autoimmune Disease: Pathogenesis and Treatment

Peripheral ulcerative keratitis (PUK) is type of crescent-shaped inflammatory damage that occurs in the limbal region of the cornea. PUK is always combined with an epithelial defect and the destruction of the peripheral corneal stroma. PUK may have a connection to systemic conditions, such as long-standing rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), Wegener granulomatosis (WG), relapsing polychondritis, classic polyarteritis nodosa and its variants, microscopic polyangiitis, and Churg-Strauss syndrome. However, the most common connection is with RA, which is also the focus of this review. The pathogenesis of PUK is still unclear. It is thought that circulating immune complexes and cytokines exert an important influence on the progression of this syndrome. Treatment is applied to inhibit certain aspects of PUK pathogenesis

Inhibition of Zymosan-Induced Inflammatory Factors Expression by ATRA Nanostructured Lipid Carriers

Purpose. The study aimed to evaluate the effect of all-trans retinoic acid-loaded nanostructured lipid carriers (ATRA-NLCs) on the zymosan-induced expression of the cytokines IL-4, IL-10, and IFN-γ and the matrix metalloproteinases/tissue inhibitor of metalloproteinases (MMPs/TIMPs) and TLR2 in rabbit corneal fibroblasts (RCFs). Methods. ATRA-NLCs were prepared by emulsification. RCFs were isolated and harvested after four to seven passages in monolayer culture. Cytokine release (IL-4, IL-10, and IFN-γ) induced by zymosan was analyzed by cytokine release assay, reverse transcription, and real-time polymerase chain reaction (RT-PCR) analysis detection. MMP-1, MMP-3, and MMP-13, TIMP-1 and TIMP-2, and TLR2 expression were analyzed by immunoblotting. Results. ATRA-NLCs were resistant to light and physically stable, and the average size of the ATRA-NLCs was 200 nm. ATRA-NLCs increased the zymosan-induced release of IL-4 and IL-10 and decreased the release of IFN-γ by RCFs. ATRA-NLCs decreased the levels of TLR2 and MMPs/TIMPs above. Conclusions. ATRA may be a potent anti-inflammatory agent for the therapy of fungal keratitis (FK)

Blind Fault Extraction of Rolling-Bearing Compound Fault Based on Improved Morphological Filtering and Sparse Component Analysis

In order to effectively separate and extract bearing composite faults, in view of the non-linearity, strong interference and unknown number of fault source signals of the measured fault signals, a composite fault-diagnosis blind extraction method based on improved morphological filtering of sinC function (SMF), density peak clustering (DPC) and orthogonal matching pursuit (OMP) is proposed. In this method, the sinC function is used as the structural element of the morphological filter for the first time to improve the traditional morphological filter. After the observation signal is processed by the improved morphological filter, the impact characteristics of the signal are improved, and the signal meets the sparsity. Then, on the premise that the number of clustering is unknown, the density peak algorithm is used to cluster sparse signals to obtain the clustering center, which is equivalent to the hybrid matrix. Finally, the hybrid matrix is transformed into a sensing matrix, and the signal is transformed into the frequency domain to complete the compressive sensing and reconstruction of the signal in the frequency domain. Both simulation and measured signal results show that this algorithm can effectively complete the blind separation of rolling bearing faults when the number of fault sources is unknown, and the time cost can be reduced by about 75%

Vibration-based Damage Detection to the Composite Tank Filled with Fluid

International audienc

Detection of Ultrasonic Stress Waves in Structures Using 3D Shaped Optic Fiber Based on a Mach–Zehnder Interferometer

This work proposes a 3D shaped optic fiber sensor for ultrasonic stress waves detection based on the principle of a Mach–Zehnder interferometer. This sensor can be used to receive acoustic emission signals in the passive damage detection methods and other types of ultrasonic signals propagating in the active damage detection methods, such as guided wave-based methods. The sensitivity of an ultrasonic fiber sensor based on the Mach–Zehnder interferometer mainly depends on the length of the sensing optical fiber; therefore, the proposed sensor achieves the maximum possible sensitivity by wrapping an optical fiber on a hollow cylinder with a base. The deformation of the optical fiber is produced by the displacement field of guided waves in the hollow cylinder. The sensor was first analyzed using the finite element method, which demonstrated its basic sensing capacity, and the simulation signals have the same characteristics in the frequency domain as the excitation signal. Subsequently, the primary investigations were conducted via a series of experiments. The sensor was used to detect guided wave signals excited by a piezoelectric wafer in an aluminum plate, and subsequently it was tested on a reinforced concrete beam, which produced acoustic emission signals via impact loading and crack extension when it was loaded to failure. The signals obtained from a piezoelectric acoustic emission sensor were used for comparison, and the results indicated that the proposed 3D fiber optic sensor can detect ultrasonic signals in the specific frequency response range

Interface monitoring of steel-concrete-steel sandwich structures using piezoelectric transducers

Steel-concrete-steel (SCS) sandwich structures have important advantages over conventional concrete structures, however, bond-slip between the steel plate and concrete may lead to a loss of composite action, resulting in a reduction of stiffness and fatigue life of SCS sandwich structures. Due to the inaccessibility and invisibility of the interface, the interfacial performance monitoring and debonding detection using traditional measurement methods, such as relative displacement between the steel plate and core concrete, have proved challenging. In this work, two methods using piezoelectric transducers are proposed to detect the bond-slip between steel plate and core concrete during the test of the beam. The first one is acoustic emission (AE) method, which can detect the dynamic process of bond-slip. AE signals can be detected when initial micro cracks form and indicate the damage severity, types and locations. The second is electromechanical impedance (EMI) method, which can be used to evaluate the damage due to bond-slip through comparing with the reference data in static state, even if the bond-slip is invisible and suspends. In this work, the experiment is implemented to demonstrate the bond-slip monitoring using above methods. Experimental results and further analysis show the validity and unique advantage of the proposed methods. Keywords: Steel-concrete-steel sandwich structures, Bond-slip, Interface monitoring, Acoustic emission, Electromechanical impedanc

Generalized subspace identification for rotating systems: application to a wind turbine

International audienc

Data-Driven Damage Classification Using Guided Waves in Pipe Structures

Damage types are important for structural condition assessment, however, for conventionally guided wave-based inspections, the characteristics extracted from the guided wave packets are usually used to detect, locate and quantify the damages, but not classify them. In this work, the data-driven method is proposed to classify the common damages in the pipe utilizing the guided wave signals obtained from numerous damage detection tests. The fundamental torsional mode T(0,1) is selected to conduct the guided wave-based damage detection to reduce the complexity of signal processing for its almost non-dispersive property. A total of 520 groups of experimental data under different degrees of damage were obtained to verify the proposed method. Finally, with help of a deep neural network (DNN) algorithm, all response data from the damages in the pipes were all clearly classified with quite high probability

A Novel Adaptive Time-Frequency Filtering Approach to Enhance the Ultrasonic Inspection of Stainless Steel Structures

Ultrasonic nondestructive testing (NDT) provides a valuable insight into the integrity of stainless steel structures, but the noise caused by the scattering of stainless steel microstructure often limits the effectiveness of inspection. This work presents a novel adaptive filtering approach to enhance the signal-to-noise ratio (SNR) of a measured ultrasonic signal from the inspection of a stainless steel component, enabling the detection of hidden flaws under strong noise. After the spectral modeling of the noisy ultrasonic NDT signal, the difference between the spectral characteristics of a flaw echo and that of grain noise is highlighted, and a reference spectrum model to estimate the frequency spectrum of the echo reflected by any possible flaw is developed. Then, the signal is segmented and the similarity between the spectra of data segments and the reference spectra is evaluated quantitatively by the spectral similarity index (SSI). Based on this index, an adaptive time-frequency filtering scheme is proposed. Each data segment is processed by the filtering to suppress the energy of noise. The processed data segments are recombined to generate the de-noised signal after multiplying weighting coefficients, which again is determined by the SSI. The performance of the proposed method for SNR enhancement is evaluated by both the simulated and experimental signal and the effectiveness has been successfully demonstrated